Conceptual congruency effects are biases induced by an irrelevant conceptual dimension of a task (e.g., location in vertical space) on the processing of another, relevant dimension (e.g., judging words' emotional evaluation). Such effects are a central empirical pillar for recent views about how the mind ⁄ brain represents concepts. In the present paper, we show how attentional cueing (both exogenous and endogenous) to each conceptual dimension succeeds in modifying both the manifestation and the symmetry of the effect. The theoretical implications of this finding are discussed.

Abstract. This study explores the reasons why colour words and emotion words are frequently associated in the different languages of the world. One of them is connotative overlap between the colour term and the emotion term. A new experimental methodology, the Implicit Association Test (IAT), is used to investigate the implicit connotative structure of the Peninsular Spanish colour terms rojo (red), azul (blue), verde (green) and amarillo (yellow) in terms of Osgood's universal semantic dimensions: Evaluation (good-bad), Activity (excited-relaxed) and Potency (strong-weak). The results show a connotative profile compatible with the previous literature, except for the valence (good -bad) of some of the colour terms, which is reversed. We suggest reasons for both these similarities and differences with previous studies and propose further research to test these implicit connotations and their effect on the association of colour with emotion words.

RÉSUMÉSlobin a remarqué les différences entre les verbes de mouvement en espagnol et en anglais, en ce qui concerne l'expression d'éléments comme le « trajet du mouvement », ou sa « modalité ». En général, les verbes anglais incorporent la « modalité » dans le noyau de leur sens alors que les verbes espagnols ont tendance à incorporer le « trajet » et à exprimer la « modalité » avec un complément additionnel. En comparant les évènements de mouvement en anglais et leurs traductions en espagnol dans différents romans, Slobin a observé que seulement 51% des verbes exprimant la modalité étaient traduits à l'espagnol par des verbes de manière, les autres demeurant neutralisés ou omis. Nous avons l'intention d'appliquer l'analyse de Slobin aux verbes de langue, en anglais et en espagnol. Notre travail étudiera les systèmes d'agglutination des verbes en anglais et en espagnol ainsi que la méthode de traduction de ces verbes adoptée par les traducteurs espagnols.ABSTRACT Slobin (1997Slobin ( , 1998 has pointed out the differences between Spanish and English verbs of motion with regard to the expression of elements such as "Path of motion" or "Manner of motion." Generally speaking, English verbs incorporate manner to their core meaning while Spanish verbs tend to incorporate Path, expressing Manner with an additional complement. Comparing English motion events and their translation into Spanish in several novels, Slobin found out that only 51% of English manner verbs were translated into Spanish manner verbs (Slobin 1996), the rest being neutralized or omitted. We intend to apply Slobin's analysis to verbs of saying in English and Spanish. Our work aims to analyze the conflation patterns of verbs of saying in English and Spanish and the way Spanish translators deal with them. MOTS-CLÉS/KEY WORDSframe semantics, manner of motion, manner verbs, novels, path of motion

Research into the multimodal dimensions of human communication faces a set of distinctive methodological challenges. Collecting the datasets is resource-intensive, analysis often lacks peer validation, and the absence of shared datasets makes it difficult to develop standards. External validity is hampered by small datasets, yet large datasets are intractable. Red Hen Lab spearheads an international infrastructure for data-driven multimodal communication research, facilitating an integrated cross-disciplinary workflow. Linguists, communication scholars, statisticians, and computer scientists work together to develop research questions, annotate training sets, and develop pattern discovery and machine learning tools that handle vast collections of multimodal data, beyond the dreams of previous researchers. This infrastructure makes it possible for researchers at multiple sites to work in real-time in transdisciplinary teams. We review the vision, progress, and prospects of this research consortium.

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